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Record W7117301010 · doi:10.61782/fa.2025.0794

Soundcool: Enabling Easy-to-Configure International Collaborative Performances with Minimal Technical Knowledge and AI

2025· article· W7117301010 on OpenAlexfundno aff
Jorge Sastre, Francisco Valero-García, Roger Dannenberg, Julio Andrés, Stefano Scarani, Nuria Lloret

Bibliographic record

Venuenot available
Typearticle
Language
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsnot available
FundersGeneralitat ValencianaYork University
KeywordsField (mathematics)Set (abstract data type)Key (lock)Feature (linguistics)Process (computing)

Abstract

fetched live from OpenAlex

Soundcool is a modular multimedia system designed for collaborative performances with minimal technical requirements.Originally developed for educational purposes, it has evolved into a powerful tool for professional music and audiovisual productions.Its intuitive interface allows users to configure and control modules remotely, enabling international performances with ease.During the COVID-19 pandemic, Soundcool's remote capabilities were leveraged for distributed concerts, such as the piece "Poliacordes Audiovisuales," demonstrating its potential for creative collaboration.In this paper, we explore its applications for live networked performances and international educational projects, highlighting its role in democratizing multimedia creation.Additionally, we present the latest version, Soundcool 5.1, which introduces videomapping with minimal hardware requirements.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.886
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.276
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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